Beneficial Relative Permeabilities for Polymer Flooding
Bibliographic record
Abstract
Abstract This paper examines oil displacement as a function of polymer solution viscosity during laboratory studies in support of a polymer flood in the Cactus Lake reservoir in Canada. When displacing 1610-cp crude oil from field cores (at 27°C and 1 ft/d), oil recovery efficiency increased with polymer solution viscosity up to 25 cp (7.3 s-1). No significant benefit was noted from injecting polymer solutions more viscous than 25 cp. Much of the paper explores why this result occurred. That is, was it due to the core, the oil, the saturation history, the relative permeability characteristics, emulsification, or simply the nature of the test? Floods in field cores examined relative permeability for different saturation histories—including native state, cleaned/water-saturated first, and cleaned/oil-saturated first. In addition to the field cores and crude oil, studies were performed using hydrophobic (oil-wet) polyethylene cores and refined oils with viscosities ranging from 2.9 to 1000 cp. In nine field cores, relative permeability to water (krw) remained low—less than 0.03 for water saturations up to 0.42. Relative permeability to oil (kro) remained reasonably high (greater than 0.05) for most of this range. At a given water saturation, krw values for 1000-cp crude oil were about ten times lower than for 1000-cp refined oil. These observations help explain why only 25- cp polymer solutions were effective in recovering the viscous crude oil. In contrast to results found for the Daqing polymer flood, no evidence was found that high-molecular-weight (Mw) HPAM solutions mobilized trapped residual oil in our application. The results are discussed in light of ideas expressed in recent publications. The relevance of the results to field applications is also examined. Although 25-cp polymer solutions were effective in displacing oil during our core floods, the choice of polymer viscosity for a field application must consider reservoir heterogeneity and the risk of channeling/viscous fingering in a reservoir.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".